{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l\n",
    "\n",
    "T = 1000\n",
    "time = torch.arange(1,T +1,dtype=torch.float32)\n",
    "x = torch.sin(0.01 * time) + torch.normal(0,0.2,(T,))\n",
    "d2l.plot(time,[x],'time','x',xlim=[1,1000],figsize=(6,3))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "tau = 4\n",
    "features = torch.zeros((T - tau,tau))\n",
    "for i in range(tau):\n",
    "    features[:,i] = x[i:T - tau +i]\n",
    "labels = x[tau:].reshape((-1,1))\n",
    "\n",
    "batch_size,n_train = 16,600\n",
    "train_iter = d2l.load_array((features[:n_train],labels[:n_train]),batch_size,is_train=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def init_weights(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.xavier_uniform_(m.weight)\n",
    "\n",
    "def get_net():\n",
    "    net = nn.Sequential(nn.Linear(4,10),nn.ReLU(),nn.Linear(10,1))\n",
    "    net.apply(init_weights)\n",
    "    return net\n",
    "\n",
    "\n",
    "loss = nn.MSELoss(reduction='none')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def train(net,train_iter,loss,epochs,lr):\n",
    "    trainer = torch.optim.Adam(net.parameters(),lr)\n",
    "    for epoch in range(epochs):\n",
    "        for X,y in train_iter:\n",
    "            trainer.zero_grad()\n",
    "            l = loss(net(X),y)\n",
    "            l.sum().backward()\n",
    "            trainer.step()\n",
    "        print(f'epoch {epoch +1}, loss:{d2l.evaluate_loss(net,train_iter,loss):f}')\n",
    "net = get_net()\n",
    "train(net,train_iter,loss,5,0.01)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "onestep_preds = net(features)\n",
    "d2l.plot([time,time[tau:]],[x.detach().numpy(),onestep_preds.detach().numpy()],'time','x',legend=['data','1-step preds'],xlim=[1,1000],figsize=(6,3))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "tau = 4\n",
    "features = torch.zeros((T - tau,tau))\n",
    "for i in range(tau):\n",
    "    features[:,i] = x[i:T - tau +i]\n",
    "labels = x[tau:].reshape((-1,1))\n",
    "\n",
    "batch_size,n_train = 16,600\n",
    "train_iter = d2l.load_array((features[:n_train],labels[:n_train]),batch_size,is_train=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def init_weights(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.xavier_uniform_(m.weight)\n",
    "\n",
    "def get_net():\n",
    "    net = nn.Sequential(nn.Linear(4,10),nn.ReLU(),nn.Linear(10,1))\n",
    "    net.apply(init_weights)\n",
    "    return net\n",
    "\n",
    "\n",
    "loss = nn.MSELoss(reduction='none')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def train(net,train_iter,loss,epochs,lr):\n",
    "    trainer = torch.optim.Adam(net.parameters(),lr)\n",
    "    for epoch in range(epochs):\n",
    "        for X,y in train_iter:\n",
    "            trainer.zero_grad()\n",
    "            l = loss(net(X),y)\n",
    "            l.sum().backward()\n",
    "            trainer.step()\n",
    "        print(f'epoch {epoch +1}, loss:{d2l.evaluate_loss(net,train_iter,loss):f}')\n",
    "net = get_net()\n",
    "train(net,train_iter,loss,5,0.01)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "onestep_preds = net(features)\n",
    "d2l.plot([time,time[tau:]],[x.detach().numpy(),onestep_preds.detach().numpy()],'time','x',legend=['data','1-step preds'],xlim=[1,1000],figsize=(6,3))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "tau = 4\n",
    "features = torch.zeros((T - tau,tau))\n",
    "for i in range(tau):\n",
    "    features[:,i] = x[i:T - tau +i]\n",
    "labels = x[tau:].reshape((-1,1))\n",
    "\n",
    "batch_size,n_train = 16,600\n",
    "train_iter = d2l.load_array((features[:n_train],labels[:n_train]),batch_size,is_train=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def init_weights(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.xavier_uniform_(m.weight)\n",
    "\n",
    "def get_net():\n",
    "    net = nn.Sequential(nn.Linear(4,10),nn.ReLU(),nn.Linear(10,1))\n",
    "    net.apply(init_weights)\n",
    "    return net\n",
    "\n",
    "\n",
    "loss = nn.MSELoss(reduction='none')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def train(net,train_iter,loss,epochs,lr):\n",
    "    trainer = torch.optim.Adam(net.parameters(),lr)\n",
    "    for epoch in range(epochs):\n",
    "        for X,y in train_iter:\n",
    "            trainer.zero_grad()\n",
    "            l = loss(net(X),y)\n",
    "            l.sum().backward()\n",
    "            trainer.step()\n",
    "        print(f'epoch {epoch +1}, loss:{d2l.evaluate_loss(net,train_iter,loss):f}')\n",
    "net = get_net()\n",
    "train(net,train_iter,loss,5,0.01)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "onestep_preds = net(features)\n",
    "d2l.plot([time,time[tau:]],[x.detach().numpy(),onestep_preds.detach().numpy()],'time','x',legend=['data','1-step preds'],xlim=[1,1000],figsize=(6,3))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "tau = 4\n",
    "features = torch.zeros((T - tau,tau))\n",
    "for i in range(tau):\n",
    "    features[:,i] = x[i:T - tau +i]\n",
    "labels = x[tau:].reshape((-1,1))\n",
    "\n",
    "batch_size,n_train = 16,600\n",
    "train_iter = d2l.load_array((features[:n_train],labels[:n_train]),batch_size,is_train=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def init_weights(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.xavier_uniform_(m.weight)\n",
    "\n",
    "def get_net():\n",
    "    net = nn.Sequential(nn.Linear(4,10),nn.ReLU(),nn.Linear(10,1))\n",
    "    net.apply(init_weights)\n",
    "    return net\n",
    "\n",
    "\n",
    "loss = nn.MSELoss(reduction='none')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, loss:0.046357\n",
      "epoch 2, loss:0.045678\n",
      "epoch 3, loss:0.045660\n",
      "epoch 4, loss:0.046854\n",
      "epoch 5, loss:0.047292\n"
     ]
    }
   ],
   "source": [
    "def train(net,train_iter,loss,epochs,lr):\n",
    "    trainer = torch.optim.Adam(net.parameters(),lr)\n",
    "    for epoch in range(epochs):\n",
    "        for X,y in train_iter:\n",
    "            trainer.zero_grad()\n",
    "            l = loss(net(X),y)\n",
    "            l.sum().backward()\n",
    "            trainer.step()\n",
    "        print(f'epoch {epoch +1}, loss:{d2l.evaluate_loss(net,train_iter,loss):f}')\n",
    "net = get_net()\n",
    "train(net,train_iter,loss,5,0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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   "source": [
    "onestep_preds = net(features)\n",
    "d2l.plot([time,time[tau:]],[x.detach().numpy(),onestep_preds.detach().numpy()],'time','x',legend=['data','1-step preds'],xlim=[1,1000],figsize=(6,3))"
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